Machine Learning Track

CALL FOR PAPERS

The Machine Learning Track Program Committee of the Twenty-fourth International Joint Conference on Artificial Intelligence (IJCAI-15) invites the submission of original technical papers to the Machine Learning Track of IJCAI-15. Machine Learning is a core area of artificial intelligence research, with implications and deep connections to computer perception, automated cognition and intelligent behavior. As a foundational subject, machine learning touches almost every area of artificial intelligence as a target competence or an enabling technology. Since it is a rapidly growing area that is driving pervasive technological change, machine learning has developed a variety of its own sub-fields, specialized technical tools and methodologies. To counter the perception that machine learning research is diverging from the interests of other areas of artificial intelligence, a strong presence in machine learning will be maintained at IJCAI-15. In addition to highlighting top quality research, the Machine Learning Track will also emphasize research that connects to other areas of artificial intelligence.

Submissions are therefore invited on significant, original, and previously unpublished research on all aspects of machine learning, with a special encouragement for machine learning research that demonstrates clear relevance to other areas of artificial intelligence research. General areas of interest include, but are not limited to:

deep learning and neural learning

ensemble methods

evolutionary learning and nature inspired learning

large margin and kernel methods

learning from multiple or partial labellings

learning in games and multi-agent systems

learning theory

machine learning applications

new problems

probabilistic models and methods

reinforcement learning and on-line learning

relation learning and graphs

unsupervised learning

We particularly welcome papers that formulate "new problems" for machine learning and artificial intelligence research, in addition to more traditional papers that propose "solutions" to established problems. Such "new problem" papers will be assessed in terms of the clarity of the proposed formulation, strength of the motivation, depth of the challenges identified, and feasibility of the problems. Such papers can provide value for the community identifying new paths to advancing Artificial Intelligence.We would also like to draw attention to the IJCAI-15 theme of "AI and the Arts" and encourage machine learning submissions that are relevant to this theme.

SUBMISSION DETAILS

*New* We have reached an agreement with most major conferences to jointly filter out similar papers that are submitted to multiple conferences without notifying the organizers. Please note that multiple submission are not allowed.

All papers submitted to the Machine Learning track are regarded as regular submissions to IJCAI 2015. Please consult the main IJCAI 2015 Call For Papers at http://ijcai-15.org/index.php/call-for-papers for important dates, detailed submission instructions, (including formatting guidelines and electronic templates), review process, and important policies (on multiple submissions, confidentiality and conflict of interest).

The Machine Learning Track Program Committee of the Twenty-fourth International Joint Conference on Artificial Intelligence (IJCAI-15) invites the submission of original technical papers to the Machine Learning Track of IJCAI-15. Machine Learning is a core area of artificial intelligence research, with implications and deep connections to computer perception, automated cognition and intelligent behavior. As a foundational subject, machine learning touches almost every area of artificial intelligence as a target competence or an enabling technology. Since it is a rapidly growing area that is driving pervasive technological change, machine learning has developed a variety of its own sub-fields, specialized technical tools and methodologies. To counter the perception that machine learning research is diverging from the interests of other areas of artificial intelligence, a strong presence in machine learning will be maintained at IJCAI-15. In addition to highlighting top quality research, the Machine Learning Track will also emphasize research that connects to other areas of artificial intelligence.

Submissions are therefore invited on significant, original, and previously unpublished research on all aspects of machine learning, with a special encouragement for machine learning research that demonstrates clear relevance to other areas of artificial intelligence research. General areas of interest include, but are not limited to:

‒deep learning and neural learning

‒ensemble methods

‒evolutionary learning and nature inspired learning

‒large margin and kernel methods

‒learning from multiple or partial labellings

‒learning in games and multi-agent systems

‒learning theory

‒machine learning applications.

‒probabilistic models and methods

‒reinforcement learning and on-line learning

‒relation learning and graphs

‒unsupervised learning

We particularly welcome papers that formulate "new problems" for machine learning and artificial intelligence research, in addition to more traditional papers that propose "solutions" to established problems. Such "new problem" papers will be assessed in terms of the clarity of the proposed formulation, strength of the motivation, depth of the challenges identified, and feasibility of the problems. Such papers can provide value for the community identifying new paths to advancing Artificial Intelligence.

We would also like to draw attention to the IJCAI-15 theme of "AI and the Arts" and encourage machine learning submissions that are relevant to this theme.

SUBMISSION DETAILS

All papers submitted to the Machine Learning track are regarded as regular submissions to IJCAI 2015. Please consult the main IJCAI 2015 Call For Papers at http://ijcai-15.org/index.php/call-for-papers for important dates, detailed submission instructions, (including formatting guidelines and electronic templates), review process, and important policies (on multiple submissions, confidentiality and conflict of interest).